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The outcome of the nowadays lighting system review
Lighting systems in buildings used to be less in attendance of investigators. Recently it is one of the most attractive domains. As we obtained in section 1.4, lots of innovative solution is proposing by investigators. Although, it lasts a long way to go for enhancing lighting systems. Some main common limitations characterize the whole of the above-mentioned concepts (and of the issued systems):
1. The flexibility of the lighting system in a diverse environment: The first main shortage links the leakage of lighting system flexibility regarding the diversity of the building’s indoor structure and the scale (size) to be set alight. Besides, some works are suffering from the flexibility of the lighting system to dynamic environmental conditions of the space (presence and absence of the user, outdoor light, the objective of space’s usage, etc.).
2. The other main lack relates to the specificity of the chosen technologies in aforesaid works and making their solutions hardly extendable. In fact, because of the exclusive design of the lighting systems for aforesaid spaces, it is questionable if the proposed investigations and implementations are applicable to other spaces or not.
3. Almost all the works have missed the importance of lighting elements placement in spaces. The designing of lighting elements is as much as important to choose proper lighting technology. A good balance between two will lead to an efficient lighting system. Due to the lighting system is interacting with people’s visual, the unnecessary high-performance systems took the efficient system place in most of the investigation of section 1.4.
The three above remarks indicate the guideline for covering the drawbacks of nowadays lighting systems. In this thesis, we are going to look for an approach that supports all demands for an efficient lighting system: Choosing suitable technology to cope with diverse space’s size and environmental conditions and designing lighting elements placement.
Review on different heating system’s control design
The heating system is the most consuming part of the buildings. It remains a challenging task in SBEMS due to a quite large number of diverse kinds of involved parameters and their usually nonlinear inter-dependency in the living spaces. Occupation of the living space (presence and absence of users) is one of the aforesaid parameters which can play an essential role in the heating system’s controller design. According to” black body law”, the user as a thermal object is emitting heat by radiation. Due to these facts, the need for living-spaces’ heating dynamics modeling appears as a foremost requirement for designing adaptive controllers scheming the complex behaviors of nowadays’ smart buildings. In this section, we are going to review various heating system designs and heating controllers designs. There is also reviews on modeling of heating dynamics and system identification methods. Finally, owing to the important role of occupation in heating controllers design, the third part of this section is a review of different approaches for estimating the number of people in a space.
Modeling dynamic of heating spaces and designing heating controllers
A number of works address model-free approaches coping with building’s heating. Relating conventional controllers, The authors of [Zungeru et al., 2018] introduce a control heating system for supporting the heating comfort of the user based on a very simple thermostatic controller (operating on “On/Off” strategy) with the help of a microcontroller. When the temperature is higher than the desired temperature the fan will be turned on and when the temperature is lower than the desired temperature the heater will turn on. The proposed simplistic control of the space heating operates on the difference between the desired temperature and actual temperature and could be seen as a model-free heating approach. While taking advantage of its independence from the effective complexity of the concerned edifice’s heating-dynamics, the proposed strategy is applicable to very specific homogeneous living-spaces and not be generalized to more sophisticated buildings including heterogeneous living-spaces.
In [Purdon et al., 2013], the authors propose a model-free and sensor-free HVAC control algorithm that uses simple user input (hot/cold) and adapts to changing office occupancy or ambient temperature in real-time. As an alternative, the proposed strategy includes users in the HVAC control loop through distributed smart-phone based votes about their thermal comfort for aggregated control of HVAC. The developed iterative data fusion algorithm finds optimal temperature in offices with multiple users and addresses techniques that can aggressively save energy by drifting indoor temperatures towards the outdoor temperature. The evaluation has been based on empirical data collected in 12 offices over a 3-week period and showed that the proposed control may save up to 60% of energy at a relatively small increase of 0.3°C in average occupant discomfort. While the idea is appealing, the concerned technique here also is very specific.
The designed control systems in [Zungeru et al., 2018], and [Purdon et al., 2013] operate without any pre-knowledge of the living-spaces that they are supposed to heat. In other words, the proposed solutions are based exclusively on data provided by temperature sensors within the frame of specific edifices for which the model of heating-dynamics is available. This makes the proposed models and issued controllers specific to the considered case-studies, and thus not applicable to other structures (i.e. other buildings).
There are a variety of controllers for managing the heating system. One of the widely used and well-known controllers is fuzzy controllers. In [Javaid et al. 2018], an adaptive thermostat controller model is proposed that is usable in hottest and coldest countries. The authors designed a controller based on MAMDANI and SUGENO [Researchhubs, n.d.; Ross, 2010] fuzzy techniques and wireless sensor capabilities. The proposed fuzzy controller for automating thermostat setpoints considers initialized set-point, outdoor temperature, home occupancy, and utility price (the price of electricity in the different hours of the day is different according to user’s demand) as inputs.
The system is an HVAC that is working with electricity. The authors claim, at the minimum level, their system can save 6.5% energy. They compared several approaches for controlling the set-point of the system: fixed-set point, programmable set-point, fuzzy controller based on Mamdani, fuzzy controller based on Sugeno. They concluded both fuzzy techniques are more efficient than other methods, however, Sugeno is also illustrating better results than Mamdani. The idea of a floating set point is quite appealing, however, the optimal strategies for reaching the desired setpoint can play an important role in the result that the authors did not discuss it. It seems the controller is designed based on a model-free approach. According to this fact, the authors designed a controller based on general information that they have about the system and did not face the dynamic behavior and unidentified behavior of the system.
In [Tan kok khiang, Marzuki khalid, 1999] the authors are dealing with intelligent control and monitoring of an industrial chilling and heating system. As reported by authors, predominantly PID controllers are being used for this purpose, nevertheless, considering the nonlinearity behavior of these systems, PID cannot be the best choice. Concerning this point, the authors presented a fuzzy logic controller. The proposed fuzzy logic controller has two inputs and one output. Inputs are Error and the rate of error changes and output is a control signal. Individually, input and output have 5 membership functions with triangle shape. In Figure 5, the rule-based surface is illustrated and the nonlinearity in this fuzzy controller is obvious.
As stated by the authors, the result of this work shows, the fuzzy logic controller in the case of set-point tracking has a better performance in comparison with PID controller. It should be emphasized the proposed research is a controller design of heating chilling for industrial purposes and it may not be sufficient for the places that the user comfort has the priority. Despite that, the interesting point of this work is the ability of the fuzzy controller in set-point tracking. The authors in this work for the fuzzy controller just considered error and rate of error. In the work of [Tan kok khiang, Marzuki khalid, 1999] like [Javaid et al. 2018], they designed their controller based on a model-free approach however in comparison with [Javaid et al. 2018] number of factors for inputs of the fuzzy controller is limited to two (error and rate of error) and the authors did not take into consideration other factors that can affect the system.
An investigation by [Hamed & Alami, 2015] is about an adaptive Hierarchical Fuzzy controller. They believe the hierarchical structure has several benefits:
1. The number of rules in the fuzzy logic controller will decrease
2. The system is more accurate and less complex
3. The computational part of the work will decrease.
The proposed fuzzy system has two levels and the structure of controller is composed of:
1. System: Air temperature inside the room or hall.
2. Fuzzy Level1: Control the error of varying parameters to adjust the main controller.
3. Fuzzy Level2: Is the main temperature controller.
4. Disturbances: As for opening windows or doors and CO2 concentration.
5. Temperature T0: The reference Air Temperature.
6. Temperature T1: Air Temperature after disturbances.
7. Temperature T2: Air Temperature after being heated from the heating system.
8. Temperature T3: Air Temperature inside the room.
9. Desired performance: The rules of fuzzy controllers from system experts (technicians).
As many traditional closed-loop controllers, the control process needs to be corrected during the operation. In this investigation, the responsibility of the correction is relating to the first level of fuzzy controllers to correct the second level. The unexpected changes in the system (like entering the new fresh air in the room) can affect and make an error in the controlling process. However, because there is no feedback about that, the error (T0-T3) will not change, and also because the true nature of heat spread is slow, the air temperature is unknown just after heating the system. That is why they try to avoid this unexpected error by correcting the process. The proposed control strategy is based on compensating for indoor temperature loss. The inputs of fuzzy level 2 are error (T0-T3) and output of fuzzy level 1. The inputs to fuzzy level 1 are: T0-T1 which observes the variations that come from entering new fresh air. It comes from air quality performance; and T2-T3 that is the difference between air temperature enters the room and indoor temperature.
The Hierarchical model suggested by [Hamed & Alami, 2015] is fitting and effective in the case of reducing the number of rules. However, As the investigation is on an integrated environment and the inherent behavior of the heating systems is very slow, it is questionable how they want to measure air temperature immediately after disturbances. The author did not discuss this part. In addition, in this work according to the proposed method, the goal is to reach setpoint temperature at any cost. It may improve user comfort, however, if the user behaves out of the right framework, not only it increases the cost but also the loss of energy. In opposite to recent works, the authors have proposed a solution to face dynamic parameters that affect the system.
Another investigation based on fuzzy approaches has been done by [Omarov et al. 2017]. A heating and cooling system controller is introduced to bring a level of comfort for users and in parallel save energy. They used Mamdani technique. As the inputs of the fuzzy controller, they have injected the deviation between the actual temperature and target temperature (ΔT), (in fuzzification mode, it contains 7 membership functions), Ratio of temperature changes in a certain time interval (same membership functions as ΔT) (Figure 6) and distance from heating/cooling device to the coldest/ hot area of the room (contains three membership function), (Figure 7). The output of the system is the power of the heating/ cooling device and it is composed of two different parts. The first part is about the operating mode of the heating/ cooling system composed of six membership functions. The second part is about the speed of the fan composed of four membership functions, (Figure 9). The membership functions have a triangle shape.
The identifiers of Membership functions in Figure 6 are Large Positive Deviation (LPD), Average Positive Deviation (APD), Small Positive Deviation (SPD), Zero Deviation (Z), Small Negative Deviation (SND), Average Negative Deviation (AND), Large Negative Deviation (LND). In Figure 8 each membership function represents the variables as below:
C3: Strong cooling, C2: Average cooling, C1: Slight cooling, NO: Without changes, H1: Heating 1, H2: Heating2.
The authors remark 17% performance improvement obtained by the fuzzy controller in comparison with the on/off controller. Like recently reviewed investigations, the work of [Ekren & Küçüka, 2010] is also designed based on a model-free approach. In order to take over the problem, besides the error as an input to the fuzzy controller, the past value of the opening valve of EEV, and the frequency sent to the inverter are considered.
Table of contents :
1. State of art
1.1. Introduction
1.2. Review on the different architecture of SBEMS
1.3. The outcome of SBEMS architecture review
1.4. Review on different smart lighting systems
1.5. The outcome of the nowadays lighting system review
1.6. Review on different heating system’s control design
1.6.1. Modeling dynamic of heating spaces and designing heating controllers
1.6.2. The outcome of reviews on heating system’s control design
1.6.3. Presence estimator
1.7. Conclusion
2. The architecture of a multi-layer system for SBEMS
2.1. Introduction
2.2. The multi-layer architecture design of campus SENART
2.3. Implementation of proposed multi-layer architecture
2.3.1. Physical layer’s implementation
2.3.2. Agent layer’s implementation
2.3.3. Control layer’s implementation
2.3.4. Supervision layer’s implementation
2.4. User awareness facilities and relating results provided by SBEMS
2.5. Conclusion
3. Smart lighting system design for SBEMS
3.1. Introduction
3.2. The proposed lighting system technology and relating architecture in the SBEMS
3.3. The proposed method for designing lighting elements placement architecture
3.4. Case study: Example of LE placement design simulation and first evaluation
3.5. Validations, results, and evaluation
3.5.1. Validation of simulation
3.5.2. Results, validation, and evaluation of the proposed lighting system on building A of UPEC SENART campus
The energy efficiency of the proposed lighting system
3.6. Conclusion
4. Adaptive heating controller for SBEMS
4.1. Introduction
4.2. Identification of heating dynamics of building living space
4.2.1. Introduction
4.2.2. Machine-Learning based identification of the heating dynamics of the living-space
4.2.3. Implementation of the proposed living-spaces’ dynamic heating model
4.2.4. Experimentation and results
Experimental Results
4.2.5. Conclusion on the identification of heating dynamics of building living space
4.3. Fuzzy heating controller design
4.3.1. Introduction
4.3.2. Fuzzy controller for facing control of living-spaces heating dynamics
4.3.3. Control strategy
4.3.4. Simulation, and evaluation of the fuzzy controller in comparison with On/Off control strategy 104 Experimental simulation in the theoretical classroom with a medium size (living-spaces of building A1)
Experimental simulation in the theoretical classroom with a medium size (living-spaces of building A2)
Experimental simulation of the practical classroom with a large size (living-spaces of building A1)
Experimental simulation of the practical classroom with a large size (living-spaces of building A2)
Experimental simulation office room with a small size (living-spaces of building A1) 112
Experimental simulation office room with a small size (living-spaces of building A2) 113
4.3.5. Results and validation:
4.4. Presence estimator
4.4.1. Introduction to presence estimator
4.4.2. The proposed Virtual presence estimator sensor based on the fuzzy inference
4.4.3. Implementation of the proposed presence estimator in building A of SENART campus
4.4.4. experimentation, result, and evaluation of virtual presence estimator
4.5. The architecture and evaluation of SBEMS regarding the heating fuzzy controller with presence estimator
4.6. Simulation, and evaluation of the fuzzy controller with virtual presence estimator as an occupation counter
4.7. Conclusion
General conclusion and perspective
